Disease Diagnostics Research Articles

Extraction of high-molecular-weight DNA from Streptococcus spp. for nanopore sequencing in resource-limited settings.

The long-read sequencing platform MinION, developed by Oxford Nanopore Technologies, enables the sequencing of bacterial genomes in resource-limited settings, such as field conditions or low- and middle-income countries. For this purpose, protocols for extracting high-molecular-weight DNA using nonhazardous, inexpensive reagents and equipment are needed, and some methods have been developed for gram-negative bacteria. However, we found that without modification, these protocols are unsuitable for gram-positive Streptococcus spp., a major threat to fish farming and food security in low- and middle-income countries. Multiple approaches were evaluated, and the most effective was an extraction method using lysozyme, sodium dodecyl sulfate, and proteinase K for lysis of bacterial cells and magnetic beads for DNA recovery. We optimized the method to consistently achieve sufficient yields of pure high-molecular-weight DNA with minimal reagents and time and developed a version of the protocol which can be performed without a centrifuge or electrical power. The suitability of the method was verified by MinION sequencing and assembly of 12 genomes of epidemiologically diverse fish-pathogenic Streptococcus iniae and Streptococcus agalactiae isolates. The combination of effective high-molecular-weight DNA extraction and MinION sequencing enabled the discovery of a naturally occurring 15 kb low-copy number mobilizable plasmid in S. iniae, which we name pSI1. We expect that our resource-limited settings-adapted protocol for high-molecular-weight DNA extraction could be implemented successfully for similarly recalcitrant-to-lysis gram-positive bacteria, and it represents a method of choice for MinION-based disease diagnostics in low- and middle-income countries.

Flexible Scaffold Modulation of Spatial Structure and Function of Hierarchically Porous Nanoparticle@ZIF-8 Composites to Enhance Field Deployable Disease Diagnostics.

Catalytic nanoparticle@metal-organic framework (MOF) composites have attracted significant interest in point-of-care testing (POCT) owing to their prominent catalytic activity. However, the trade-off between high loading efficiency and high catalytic activity remains challenging because high concentrations of nanoparticles tend to cause the misjoining and collapse of the MOFs. Herein, a facile strategy is reported to encapsulate high concentrations of platinum (Pt) nanoparticles into zeolitic imidazolate framework-8 (ZIF-8) using polydopamine (PDA) as a support for Pt@ZIF-8 and as a flexible scaffold for further immobilization of Pt nanoparticles. The resulting composite (Pt@ZIF-8@PDA@Pt) exhibits ultrahigh Pt nanoparticle loading efficiency, exceptional catalytic activity, stability, and a bright colorimetric signal. Following integration with lateral flow immunoassay (LFIA), the detection limits for pre- and post-catalysis detection of B-type natriuretic peptide (NT-proBNP) are 0.18 and 0.015ng mL-1, respectively, representing a 6-fold and 70-fold improvement compared to gold nanoparticle-based LFIA. Moreover, Pt@ZIF-8@PDA@Pt-based LFIA achieves 100% diagnostic sensitivity for NT-proBNP in a cohort of 184 clinical samples.

Neighborhood structure-guided brain functional networks estimation for mild cognitive impairment identification.

The adoption and growth of functional magnetic resonance imaging (fMRI) technology, especially through the use of Pearson's correlation (PC) for constructing brain functional networks (BFN), has significantly advanced brain disease diagnostics by uncovering the brain's operational mechanisms and offering biomarkers for early detection. However, the PC always tends to make for a dense BFN, which violates the biological prior. Therefore, in practice, researchers use hard-threshold to remove weak connection edges or introduce l 1-norm as a regularization term to obtain sparse BFNs. However, these approaches neglect the spatial neighborhood information between regions of interest (ROIs), and ROI with closer distances has higher connectivity prospects than ROI with farther distances due to the principle of simple wiring costs in resent studies. Thus, we propose a neighborhood structure-guided BFN estimation method in this article. In detail, we figure the ROIs' Euclidean distances and sort them. Then, we apply the K-nearest neighbor (KNN) to find out the top K neighbors closest to the current ROIs, where each ROI's K neighbors are independent of each other. We establish the connection relationship between the ROIs and these K neighbors and construct the global topology adjacency matrix according to the binary network. Connect ROI nodes with k nearest neighbors using edges to generate an adjacency graph, forming an adjacency matrix. Based on adjacency matrix, PC calculates the correlation coefficient between ROIs connected by edges, and generates the BFN. With the purpose of evaluating the performance of the introduced method, we utilize the estimated BFN for distinguishing individuals with mild cognitive impairment (MCI) from the healthy ones. Experimental outcomes imply this method attains better classification performance than the baselines. Additionally, we compared it with the most commonly used time series methods in deep learning. Results of the performance of K-nearest neighbor-Pearson's correlation (K-PC) has some advantage over deep learning.

Ovarian neoplasms in pediatric gynecology (literature review)

Ovarian neoplasms in children and adolescents are rare pathologies (2.6–3.0 per 100,000 patients a year), however, they are sufficiently relevant in pediatric gynecology nowadays. The age of the patients highly correlates with different histological types of ovarian neoplasms. And this is a reason for discussing this disease in different age groups apart. Unfortunately, there are a number of unresolved issues connected with the etiology, pathogenesis and diagnosis. This paper discusses recent information related with the subject “ovarian neoplasms in pediatric gynecology” and particularly questions which require further study.The aim of the work is to conduct a review of recent literature which corresponds to this subject and to discover questions which require further study.The analysis revealed that the etiology and pathogenesis of a number of rare neoplasms has not been discovered fully yet and that can be the cause of the delayed disease diagnostics. Apart from this there are no tools for the preoperative stratification of the risk of malignancy and that can lead to more frequent radical operations for benign tumors.

Fundamentals, Synthetic Strategies and Applications of Non-Covalently Imprinted Polymers.

Molecular imprinting has emerged as an important and practical technology to create economical and stable synthetic mimics of antibodies and enzymes. It has already found a variety of important applications, such as affinity separation, chemical/biological sensing, disease diagnostics, proteomics, bioimaging, controlled drug release, and catalysis. In the past decade, significant breakthroughs have been made in non-covalently imprinted polymers, from their synthesis through to their applications. In terms of synthesis, quite a few versatile and facile imprinting approaches for preparing MIPs have been invented, which have effectively solved some key issues in molecular imprinting. Additionally, important applications in several areas, such as sensors, proteomics and bioimaging, have been well demonstrated. In this review, we critically and comprehensively survey key recent advances made in the preparation of non-covalently imprinted polymers and their important applications. We focus on the state-of-art of this technology from three different perspectives: fundamentals, synthetic strategies, and applications. We first provide a fundamental basis for molecular imprinting technologies that have been developed, which is extremely helpful for establishing a sound understanding of the challenges in molecular imprinting. Then, we discuss in particular the major breakthroughs within the last ten years (2014-2024), with emphasis on new imprinting approaches, what strengths the breakthroughs can provide, and which new applications the properties of the prepared non-covalently imprinted polymers are fit for.

Modern Methods and Prospects for Using Artificial Intelligence in Disease Diagnostics: A Narrative Review

Modern Methods and Prospects for Using Artificial Intelligence in Disease Diagnostics: A Narrative Review

DeepPGD: A Deep Learning Model for DNA Methylation Prediction Using Temporal Convolution, BiLSTM, and Attention Mechanism.

As part of the field of DNA methylation identification, this study tackles the challenge of enhancing recognition performance by introducing a specialized deep learning framework called DeepPGD. DNA methylation, a crucial biological modification, plays a vital role in gene expression analyses, cellular differentiation, and the study of disease progression. However, accurately and efficiently identifying DNA methylation sites remains a pivotal concern in the field of bioinformatics. The issue addressed in this paper is the presence of methylation in DNA, which is a binary classification problem. To address this, our research aimed to develop a deep learning algorithm capable of more precisely identifying these sites. The DeepPGD framework combined a dual residual structure involving Temporal convolutional networks (TCNs) and bidirectional long short-term memory (BiLSTM) networks to effectively extract intricate DNA structural and sequence features. Additionally, to meet the practical requirements of DNA methylation identification, extensive experiments were conducted across a variety of biological species. The experimental results highlighted DeepPGD's exceptional performance across multiple evaluation metrics, including accuracy, Matthews' correlation coefficient (MCC), and the area under the curve (AUC). In comparison to other algorithms in the same domain, DeepPGD demonstrated superior classification and predictive capabilities across various biological species datasets. This significant advancement in algorithmic prowess not only offers substantial technical support, but also holds potential for research and practical implementation within the DNA methylation identification domain. Moreover, the DeepPGD framework shows potential for application in genomics research, biomedicine, and disease diagnostics, among other fields.

Levonorgestrel protected Au10 cluster for hypochlorite sensing in living organisms

BackgroundHighly reactive oxygen species (ROS) could lead to serious damage in living cells and are associated with many diseases like cancers. Metal cluster with strong fluorescence has great potential in biosensing and many thiolate ligands-protected clusters have been applied in ROS sensing. ResultsIn this work, we synthesized levonorgestrel protected Au10 cluster with specific sensing ability for highly ROS via crystal transformation from Au8 cluster, demonstrating the significance of inner core structure on detecting performance. The detection limit of Au10 cluster for ClO− could reach as low as 0.1 μM. This fluorescent probe not only achieving detection of exogenous ClO− in living cells and zebrafish, but also successful imaging of endogenous ClO− in HeLa and HepG2 cells. SignificanceIn comparison to previously reported cluster-based sensors for ROS, this work proposes a different reaction mechanism of metal nanoclusters for ROS detection (breakage of gold-alkynyl bond and oxidation of alkynyl group). This provides new directions for designing specific ROS probes and broadens the applications of metal clusters in disease diagnostics.

Assessment of Deep Learning Models for Poultry Disease Detection and Diagnostics: A Survey Paper

This study focuses on the assessment of a deep learning model for the detection and diagnostics of poultry diseases. The model utilizes a convolutional neural network architecture to automatically analyze images of diseased poultry and accurately classify the type of disease present. The performance of the model is evaluated by comparing its predictions with expert- annotated data. The results show that the deep learning model achieves high accuracy in detecting common poultry diseases, outperforming traditional methods. This novel approach has the potential to revolutionize the field of poultry healthcare by providing fast and accurate diagnostics, leading to improved disease management and welfare for poultry populations.

Genomic Landscape of Branchio-Oto-Renal Syndrome through Whole-Genome Sequencing: A Single Rare Disease Center Experience in South Korea.

Branchio-oto-renal (BOR) and branchio-otic (BO) syndromes are characterized by anomalies affecting the ears, often accompanied by hearing loss, as well as abnormalities in the branchial arches and renal system. These syndromes exhibit a broad spectrum of phenotypes and a complex genomic landscape, with significant contributions from the EYA1 gene and the SIX gene family, including SIX1 and SIX5. Due to their diverse phenotypic presentations, which can overlap with other genetic syndromes, molecular genetic confirmation is essential. As sequencing technologies advance, whole-genome sequencing (WGS) is increasingly used in rare disease diagnostics. We explored the genomic landscape of 23 unrelated Korean families with typical or atypical BOR/BO syndrome using a stepwise approach: targeted panel sequencing and exome sequencing (Step 1), multiplex ligation-dependent probe amplification (MLPA) with copy number variation screening (Step 2), and WGS (Step 3). Integrating WGS into our diagnostic pipeline detected structure variations, including cryptic inversion and complex genomic rearrangement, eventually enhancing the diagnostic yield to 91%. Our findings expand the genomic architecture of BOR/BO syndrome and highlight the need for WGS to address the genetic diagnosis of clinically heterogeneous rare diseases.

Fluorescent covalent organic framework as an ultrasensitive fluorescent probe for tyrosinase activity monitoring and inhibitor screening

BackgroundAs a significant biomarker of melanocytic lesions, tyrosinase (TYR) plays an essential role in the clinical diagnosis and treatment of melanin-related diseases. Thus, it is important to develop robust methods for assessing TYR activity. Covalent organic frameworks (COFs) have garnered considerable attention owing to their unique properties, including high chemical stability, good biocompatibility, and large surface area compared with organic dyes, noble metal nanoclusters, and semiconductor quantum dots. However, most COFs are insoluble in water and exhibit weak or no fluorescence emission. Therefore, the development of a water-soluble fluorescent COF for detecting TYR activity in biological samples remains highly desired. ResultsIn this work, a sensitive and facile fluorometric method based on fluorescent COF was constructed for the detection of TYR activity in human serum samples. The water-soluble COF was fabricated through the condensation polymerization of 4′,4‴,4′′′′′,4′′′′′′′-(1,2-ethene-diylidene) tetrakis [1,1′-biphenyl]-4-carboxaldehyde and 2,4,6-tris-(4-aminophenyl)-triazine. The resulting COF displayed yellow-green fluorescence with a maximum emission peak at 560 nm. Tyrosine was catalyzed by TYR to produce melanin-like polymers which formed a coating on the surface of COF and effectively quenched its fluorescence due to fluorescence resonance energy transfer. The proposed approach demonstrated a strong linear correlation in the range of 0.5–80 U/L with a low detection limit of 0.09 U/L. Additionally, the limit of detection for kojic acid, serving as a representative TYR inhibitor, was determined to be 0.0004 μg/mL. SignificanceOur proposed fluorometric sensing platform exhibited exceptional selectivity, sensitivity, and satisfactory recoveries in human serum samples, which is of paramount importance for the clinical diagnostics of melanin-related diseases. Furthermore, the proposed approach was further employed for the screening of TYR inhibitors, suggesting the potential applications in clinical treatment and pharmaceutical research.

Advancing Pathogen Identification: The Role of Digital PCR in Enhancing Diagnostic Power in Different Settings.

Digital polymerase chain reaction (dPCR) has emerged as a groundbreaking technology in molecular biology and diagnostics, offering exceptional precision and sensitivity in nucleic acid detection and quantification. This review highlights the core principles and transformative potential of dPCR, particularly in infectious disease diagnostics and environmental surveillance. Emphasizing its evolution from traditional PCR, dPCR provides accurate absolute quantification of target nucleic acids through advanced partitioning techniques. The review addresses the significant impact of dPCR in sepsis diagnosis and management, showcasing its superior sensitivity and specificity in early pathogen detection and identification of drug-resistant genes. Despite its advantages, challenges such as optimization of experimental conditions, standardization of data analysis workflows, and high costs are discussed. Furthermore, we compare various commercially available dPCR platforms, detailing their features and applications in clinical and research settings. Additionally, the review explores dPCR's role in water microbiology, particularly in wastewater surveillance and monitoring of waterborne pathogens, underscoring its importance in public health protection. In conclusion, future prospects of dPCR, including methodological optimization, integration with innovative technologies, and expansion into new sectors like metagenomics, are explored.

Coordination-Driven Templated Synthesis of Hierarchically Porous Zeolitic Imidazolate Frameworks for Cascade Enzyme Cycle Amplification Coupled Immunoassay.

Although hierarchically porous zeolitic imidazolate frameworks (HPZIFs) not only inherit the intrinsic architectural and chemical stabilities of their microporous counterparts but also afford open space for the efficient mass diffusion of the macromolecules involved, their rational design and construction are still challenging. Herein, HPZIFs with tailorable pore sizes ranging from 18 to 54 nm were successfully fabricated by using a newly developed soft-template-directed strategy. Our success rooted in the fact that the screened PS81-PVP44-PEO113 triblock copolymer could effectively coordinate with the metal precursor for the directed crystallization of ZIFs along surfactant assemblies. The advantages of continuous large pores and open structures in such HPZIFs were fully taken into account to serve as a bioreactor for the efficient immunoassay. The expanded large pores provided not only a significantly vast surface area to enhance the density of capture antibodies but also enough space for accommodating multiple conjugated biomolecules in one pore channel. In combination with a cascade enzyme cycle amplification strategy, a model biomarker of prostate-specific antigen (PSA) at the femtomolar level was checked with a limit of detection of 92 fM using the developed immunosensor. Specific screening on patients with prostate cancer or even benign prostatic hyperplasia was exemplified through accurately quantifying small changes of PSA concentration in clinical serum samples, prefiguring the great potential of the developed HPZIF-8 immunosensor platform for the early monitoring and diagnostics of diseases.

Intracellular Gelation-Mediated Living Bacteria for Advanced Biotherapeutics in Mouse Models.

Despite its significant potential in various disease treatments and diagnostics, microbiotherapy is consistently plagued by multiple limitations ranging from manufacturing challenges to in vivo functionality. Inspired by the strategy involving nonproliferating yet metabolically active microorganisms, we report an intracellular gelation approach that can generate a synthetic polymer network within bacterial cells to solve these challenges. Specifically, poly(ethylene glycol dimethacrylate) (PEGDA, 700 Da) monomers are introduced into the bacterial cytosol through a single cycle of freeze-thawing followed by the initiation of intracellular free radical polymerization by UV light to create a macromolecular PEGDA gel within the bacterial cytosol. The molecular crowding resulting from intracytoplasmic gelation prohibits bacterial division and confers robust resistance to simulated gastrointestinal fluids and bile acids while retaining the ability to secrete functional proteins. Biocompatibility assessments demonstrate that the nondividing gelatinized bacteria are effective in alleviating systemic inflammation triggered by intravenous Escherichia coli injection. Furthermore, the therapeutic efficacy of gelatinized Lactobacillus rhamnosus in colitis mice provides additional support for this approach. Collectively, intracellular gelation indicates a universal strategy to manufacture next-generation live biotherapeutics for advanced microbiotherapy.

Aptamer Screening: Current Methods and Future Trend towards Non-SELEX Approach.

Aptamers are nucleic acid sequences that specifically bind with target molecules and are vital to applications such as biosensing, drug development, disease diagnostics, etc. The traditional selection procedure of aptamers is based on the Systematic Evolution of Ligands by an Exponential Enrichment (SELEX) process, which relies on repeating cycles of screening and amplification. With the rapid development of aptamer applications, RNA and XNA aptamers draw more attention than before. But their selection is troublesome due to the necessary reverse transcription and transcription process (RNA) or low efficiency and accuracy of enzymes for amplification (XNA). In light of this, we review the recent advances in aptamer selection methods and give an outlook on future development in a non-SELEX approach, which simplifies the procedure and reduces the experimental costs. We first provide an overview of the traditional SELEX methods mostly designed for screening DNA aptamers to introduce the common tools and methods. Then a section on the current screening methods for RNA and XNA is prepared to demonstrate the efforts put into screening these aptamers and the current difficulties. We further predict that the future trend of aptamer selection lies in non-SELEX methods that do not require nucleic acid amplification. We divide non-SELEX methods into an immobilized format and non-immobilized format and discuss how high-resolution partitioning methods could facilitate the further improvement of selection efficiency and accuracy.

Hybridized deep learning goniometry for improved precision in Ehlers-Danlos Syndrome (EDS) evaluation

BackgroundGeneralized Joint Hyper-mobility (GJH) can aid in the diagnosis of Ehlers-Danlos Syndrome (EDS), a complex genetic connective tissue disorder with clinical features that can mimic other disease processes. Our study focuses on developing a unique image-based goniometry system, the HybridPoseNet, which utilizes a hybrid deep learning model.ObjectiveThe proposed model is designed to provide the most accurate joint angle measurements in EDS appraisals. Using a hybrid of CNNs and HyperLSTMs in the pose estimation module of HybridPoseNet offers superior generalization and time consistency properties, setting it apart from existing complex libraries.MethodologyHybridPoseNet integrates the spatial pattern recognition prowess of MobileNet-V2 with the sequential data processing capability of HyperLSTM units. The system captures the dynamic nature of joint motion by creating a model that learns from individual frames and the sequence of movements. The CNN module of HybridPoseNet was trained on a large and diverse data set before the fine-tuning of video data involving 50 individuals visiting the EDS clinic, focusing on joints that can hyperextend. HyperLSTMs have been incorporated in video frames to avoid any time breakage in joint angle estimation in consecutive frames. The model performance was evaluated using Spearman’s coefficient correlation versus manual goniometry measurements, as well as by the human labeling of joint position, the second validation step.OutcomePreliminary findings demonstrate HybridPoseNet achieving a remarkable correlation with manual Goniometric measurements: thumb (rho = 0.847), elbows (rho = 0.822), knees (rho = 0.839), and fifth fingers (rho = 0.896), indicating that the newest model is considerably better. The model manifested a consistent performance in all joint assessments, hence not requiring selecting a variety of pose-measuring libraries for every joint. The presentation of HybridPoseNet contributes to achieving a combined and normalized approach to reviewing the mobility of joints, which has an overall enhancement of approximately 20% in accuracy compared to the regular pose estimation libraries. This innovation is very valuable to the field of medical diagnostics of connective tissue diseases and a vast improvement to its understanding.

Near-infrared polarity fluorescent probe for wash-free imaging lipid droplets and the application in the diagnosis of non-alcoholic fatty liver tissue

Lipid droplets (LDs), as primary cellular energy storage organelles, play essential roles not only in energy conservation but also in inflammatory responses, apoptosis, and the progression of diseases such as non-alcoholic fatty liver disease (NAFLD). Studies indicate that alterations in the polarity of LDs are closely linked to pathological factors like oxidative stress, ferroptosis, and lipophagy. Therefore, developing biological probes for precisely monitoring LDs polarity is crucial for understanding these biological phenomena and devising new therapeutic strategies. In this context, we synthesized four fluorescent probes (CNL1-CNL4) to precisely monitor the polarity of LDs using triphenylamine and carbazole derivatives as electron donors and isophorone and benzopyranonitrile units as electron acceptors, employing intramolecular charge transfer (ICT) mechanism. Our findings demonstrated that all four probes exhibited high sensitivity and specificity for polarity changes. Probes CNL2 and CNL4 were sensitive to polarity, and their maximum fluorescence intensity and maximum emission wavelength demonstrated a good linear relationship with polarity parameters within a certain range. Biological experiments demonstrated that both CNL2 and CNL4 possess excellent wash-free imaging capabilities and superior LDs targeting abilities but also enable dynamic monitoring of LDs and respond well to changes in LDs polarity under varying concentrations of oleic acid stimulation. Due to its longer emission wavelength (>650 nm), boasting excellent photostability, and featuring a larger Stokes shift (≈168 nm in acetonitrile), probe CNL4 was selected for further studies. Furthermore, probe CNL4 effectively monitors changes in LDs polarity under conditions of ferroptosis and oxidative stress. It has shown potential in the diagnosis of NAFLD tissue and during the process of lipophagy, highlighting its value in disease diagnostics and biological research. These findings not only deepen our understanding of the mechanisms underlying NAFLD progression but also provide new tools for diagnosing and treating related diseases.

Direct Zeptomole Detection of RNA Biomarkers by Ultrabright Fluorescent Nanoparticles on Magnetic Beads.

Nucleic acids are important biomarkers in cancer and viral diseases. However, their ultralow concentration in biological/clinical samples makes direct target detection challenging, because it leads to slow hybridization kinetics with the probe and its insufficient signal-to-noise ratio. Therefore, RNA target detection is done by molecular (target) amplification, notably by RT-PCR, which is a tedious multistep method that includes nucleic acid extraction and reverse transcription. Here, a direct method based on ultrabright dye-loaded polymeric nanoparticles in a sandwich-like hybridization assay with magnetic beads is reported. The ultrabright DNA-functionalized nanoparticle, equivalent to ≈10 000 strongly emissive rhodamine dyes, is hybridized with the magnetic bead to the RNA target, providing the signal amplification for the detection. This concept (magneto-fluorescent sandwich) enables high-throughput detection of DNA and RNA sequences of varied lengths from 48 to 1362nt with the limit of detection down to 0.3 fm using a plate reader (15 zeptomoles), among the best reported for optical sandwich assays. Moreover, it allows semi-quantitative detection of SARS-CoV-2 viral RNA directly in clinical samples without a dedicated RNA extraction step. The developed technology, combining ultrabright nanoparticles with magnetic beads, addresses fundamental challenges in RNA detection; it is expected to accelerate molecular diagnostics of diseases.

Beta-Barrel Nanopores as Diagnostic Sensors: An Engineering Perspective.

Biological nanopores are ultrasensitive and highly attractive platforms for disease diagnostics, including the sequencing of viral and microbial genes and the detection of biomarkers and pathogens. To utilize biological nanopores as diagnostic sensors, they have been engineered through various methods resulting in the accurate and highly sensitive detection of biomarkers and disease-related biomolecules. Among diverse biological nanopores, the β-barrel-containing nanopores have advantages in nanopore engineering because of their robust structure, making them well-suited for modifications. In this review, we highlight the engineering approaches for β-barrel-containing nanopores used in single-molecule sensing for applications in early diagnosis and prognosis. In the highlighted studies, β-barrel nanopores can be modified by genetic mutation to change the structure; alter charge distributions; or add enzymes, aptamers, and protein probes to enhance sensitivity and accuracy. Furthermore, this review discusses challenges and future perspectives for advancing nanopore-based diagnostic sensors.

Innovative approaches to microbial identification enhancing accuracy and speed in infectious disease diagnostics

The rapid and accurate identification of microbial pathogens is crucial for effective management of infectious diseases. Traditional methods, though reliable, are often time-consuming. Recent advancements in molecular diagnostics, mass spectrometry, and next-generation sequencing (NGS) have revolutionized the field by enhancing both the speed and accuracy of microbial identification. Polymerase chain reaction (PCR) and its derivatives, such as real-time PCR, enable rapid amplification of genetic material, significantly reducing diagnostic times. Techniques like nucleic acid sequence-based amplification (NASBA) and loop-mediated isothermal amplification (LAMP) further extend these capabilities to resource-limited settings by operating at constant temperatures. Mass spectrometry, particularly matrix-assisted laser desorption/ionization time-of-flight (MALDI-TOF), provides rapid and accurate microbial identification by analyzing protein profiles. This method has high throughput, is cost-effective, and offers the ability to detect antimicrobial resistance, significantly impacting clinical decision-making and patient management. NGS technologies offer comprehensive pathogen detection by sequencing entire microbial genomes. This approach is invaluable in diagnosing complex infections, tracking outbreaks, and identifying antibiotic resistance genes, though its high cost and complexity limit widespread use. The integration of these innovative technologies into clinical practice enhances the ability to diagnose and manage infectious diseases effectively. Molecular diagnostics, mass spectrometry, and NGS each bring unique strengths, from rapid and precise pathogen detection to comprehensive genetic analysis and resistance profiling. These advancements contribute to improved patient outcomes and more effective public health strategies. The continued development and implementation of these technologies are essential to address the evolving challenges in microbial diagnostics, paving the way for more precise and personalized approaches in infectious disease management.